An embedded vision-based system for cyclist detection and counting
DOI:
https://doi.org/10.5753/jbcs.2026.4937Keywords:
Cyclist Detection, Intelligent Transport Systems, Smart Cities, Computer Vision, Deep LearningAbstract
Automatically detecting and counting cyclists in urban scenarios is a task in intelligent transportation systems and smart cities that enables the generation of important structured data. This data contributes to understanding the dynamics of cyclists' use of the urban space and guides the development of public policies for cycling mobility and traffic safety. In this study, we propose an embedded system for cyclist detection and counting, aiming to be a lightweight solution using computer vision and deep learning methods. It is characterized by low energy consumption and easy handling, based on the Raspberry Pi 4 platform and the Edge TPU Coral accelerator. The developed system achieved an F1-score of 0.9137 for processing prerecorded video.In experiments conducted in a real urban setting, we achieved counting accuracy between 78,3% and 82,2%, a performance comparable to solutions with higher computational requirements and/or costs. Code is available at https://github.com/leandroAS86/det-cicle
Downloads
References
Aharon, N., Orfaig, R., and Bobrovsky, B.-Z. (2022). BoT-SORT: Robust associations multi-pedestrian tracking. Arxiv preprint arXiv:2206.14651. DOI: 10.48550/arXiv.2206.14651.
Allebosch, G., Van den Bossche, S., Veelaert, P., and Philips, W. (2020). Camera-based system for drafting detection while cycling. Sensors, 20(5). DOI: 10.3390/s20051241.
Beitel, D., McNee, S., McLaughlin, F., and Miranda-Moreno, L. F. (2018). Automated validation and interpolation of long-duration bicycle counting data. Transportation Research Record, 2672(43):75-86. DOI: 10.1177/0361198118783123.
Boukerche, A. and Hou, Z. (2021). Object detection using deep learning methods in traffic scenarios. ACM Comput. Surv., 54(2). DOI: 10.1145/3434398.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016). The Cityscapes dataset for semantic urban scene understanding. Arxiv preprint arXiv:1604.01685v2, abs/1604.01685. Available at:[link].
Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., and Meng, H. (2023). Strongsort: Make deepsort great again. IEEE Transactions on Multimedia, 25:8725-8737. DOI: 10.1109/TMM.2023.3240881.
García-Venegas, M., Mercado-Ravell, D. A., Pinedo-Sánchez, L. A., and Carballo-Monsivais, C. A. (2021). On the safety of vulnerable road users by cyclist detection and tracking. Mach. Vision Appl., 32(5). DOI: 10.1007/s00138-021-01231-4.
Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. DOI: 10.1109/CVPR.2012.6248074.
Guindel, C., Martin, D., and Armingol, J. M. (2018). Fast joint object detection and viewpoint estimation for traffic scene understanding. IEEE Intelligent Transportation Systems Magazine, 10(4):74-86. DOI: 10.1109/MITS.2018.2867526.
Jin, F., Sengupta, A., Cao, S., and Wu, Y.-J. (2020). MmWave radar point cloud segmentation using GMM in multimodal traffic monitoring. In 2020 IEEE International Radar Conference (RADAR), pages 732-737. DOI: 10.1109/RADAR42522.2020.9114662.
Li, K., Huang, Z., Cheng, Y.-C., and Lee, C.-H. (2014). A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4503-4507. DOI: 10.1109/ICASSP.2014.6854454.
Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., and Gavrila, D. M. (2016). A new benchmark for vision-based cyclist detection. In 2016 IEEE Intelligent Vehicles Symposium (IV), pages 1028-1033. DOI: 10.1109/IVS.2016.7535515.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T., editors, Computer Vision - ECCV 2014, pages 740-755, Cham. Springer International Publishing. DOI: 10.1007/978-3-319-10602-1_48.
Liu, C., Huynh, D. Q., Sun, Y., Reynolds, M., and Atkinson, S. (2021). A vision-based pipeline for vehicle counting, speed estimation, and classification. IEEE Transactions on Intelligent Transportation Systems, 22(12):7547-7560. DOI: 10.1109/TITS.2020.3004066.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016). SSD: Single shot multibox detector. In Leibe, B., Matas, J., Sebe, N., and Welling, M., editors, Computer Vision - ECCV 2016, pages 21-37, Cham. Springer International Publishing. DOI: 10.1007/978-3-319-46448-0_2.
Ma, H., Qiu, H., Gao, Y., Zhang, Z., Abuadbba, A., Xue, M., Fu, A., Zhang, J., Al-Sarawi, S. F., and Abbott, D. (2023). Quantization backdoors to deep learning commercial frameworks. IEEE Transactions on Dependable and Secure Computing, pages 1-18. DOI: 10.1109/TDSC.2023.3271956.
Masalov, A., Ota, J., Corbet, H., Lee, E., and Pelley, A. (2018). CyDet: Improving camera-based cyclist recognition accuracy with known cycling jersey patterns. In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 2143-2149. DOI: 10.1109/IVS.2018.8500668.
Mhalla, A., Chateau, T., Gazzah, S., and Amara, N. E. B. (2018). An embedded computer-vision system for multi-object detection in traffic surveillance. IEEE Transactions on Intelligent Transportation Systems, 20(11):4006-4018. DOI: 10.1109/TITS.2018.2876614.
Moreira, L. F. R., De F. B. Saar, L. N., Moreira, R., Rodrigues, L. G. F., Travençolo, B. A. N., and Backes, A. R. (2024). Enabling intelligence on edge through an artificial intelligence as a service architecture. In 2024 IEEE 13th International Conference on Cloud Networking (CloudNet), pages 1-8. DOI: 10.1109/CloudNet62863.2024.10815777.
Nie, X. and Wang, Y. (2025). Real-time object detection in adverse weather conditions using transformer-based architectures. International Journal of Engineering and Computer Science, 14(06):27376-27397. DOI: 10.18535/ijecs.v14i06.5171.
Oja, P., Titze, S., Bauman, A., de Geus, B., Krenn, P., Reger-Nash, B., and Kohlberger, T. (2011). Health benefits of cycling: a systematic review. Scandinavian Journal of Medicine & Science in Sports, 21(4):496-509. DOI: 10.1111/j.1600-0838.2011.01299.x.
Ozan, E., Searcy, S., Geiger, B. C., Vaughan, C., Carnes, C., Baird, C., and Hipp, A. (2021). State of the Art Approaches to Bicycle and Pedestrian Counters. NCDOT Research. Available at:[link].
Piatkowski, D. and Bopp, M. (2021). Increasing bicycling for transportation: A systematic review of the literature. Journal of Urban Planning and Development, 147(2):04021019. DOI: 10.1061/(ASCE)UP.1943-5444.0000693.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779-788. DOI: 10.1109/CVPR.2016.91.
Ren, S., He, K., Girshick, R., and Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(06):1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
Rodrigues Moreira, L. F., Moreira, R., Travençolo, B. A. N., and Backes, A. R. (2025). Deep learning based image classification for embedded devices: A systematic review. Neurocomputing, 623:129402. DOI: 10.1016/j.neucom.2025.129402.
Rodriguez-Conde, I., Campos, C., and Fdez-Riverola, F. (2021). On-device object detection for more efficient and privacy-compliant visual perception in context-aware systems. Applied Sciences, 11(19). DOI: 10.3390/app11199173.
Stahl, B., Apfelbeck, J., and Lange, R. (2023). Classification of micromobility vehicles in thermal-infrared images based on combined image and contour features using neuromorphic processing. Applied Sciences, 13(6). DOI: 10.3390/app13063795.
Tan, M., Pang, R., and Le, Q. V. (2020). EfficientDet: Scalable and efficient object detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10778-10787, Los Alamitos, CA, USA. IEEE Computer Society. DOI: 10.1109/CVPR42600.2020.01079.
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022). Bytetrack: Multi-object tracking by associating every detection box. In Avidan, S., Brostow, G., Cissé, M., Farinella, G. M., and Hassner, T., editors, Computer Vision - ECCV 2022, pages 1-21, Cham. Springer Nature Switzerland. DOI: 10.1007/978-3-031-20047-2_1.
Zou, Z., Chen, K., Shi, Z., Guo, Y., and Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3):257-276. DOI: 10.1109/JPROC.2023.3238524.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Leandro Alves dos Santos, Roberto Cesar Betini, Bogdan Tomoyuki Nassu
This work is licensed under a Creative Commons Attribution 4.0 International License.
